Package: BayesSUR 2.2-2
Zhi Zhao
BayesSUR: Bayesian Seemingly Unrelated Regression Models in High-Dimensional Settings
Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.
Authors:
BayesSUR_2.2-2.tar.gz
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BayesSUR_2.2-2.tgz(r-4.4-x86_64)BayesSUR_2.2-2.tgz(r-4.4-arm64)BayesSUR_2.2-2.tgz(r-4.3-x86_64)BayesSUR_2.2-2.tgz(r-4.3-arm64)
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BayesSUR.pdf |BayesSUR.html✨
BayesSUR/json (API)
NEWS
| # Install 'BayesSUR' in R: |
| install.packages('BayesSUR', repos = c('https://mbant.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mbant/bayessur/issues
- exampleEQTL - Simulated data set to mimic a small expression quantitative trait loci (eQTL) example
- exampleGDSC - Preprocessed data set to mimic a small pharmacogenomic example
- targetGene - TargetGene
Last updated 6 months agofrom:f600fb21fe. Checks:1 OK, 8 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 02 2025 |
| R-4.5-win-x86_64 | NOTE | Jan 02 2025 |
| R-4.5-linux-x86_64 | NOTE | Jan 02 2025 |
| R-4.4-win-x86_64 | NOTE | Jan 02 2025 |
| R-4.4-mac-x86_64 | NOTE | Jan 02 2025 |
| R-4.4-mac-aarch64 | NOTE | Jan 02 2025 |
| R-4.3-win-x86_64 | NOTE | Jan 02 2025 |
| R-4.3-mac-x86_64 | NOTE | Jan 02 2025 |
| R-4.3-mac-aarch64 | NOTE | Jan 02 2025 |
Exports:BayesSURelpdgetEstimatorplotCPOplotEstimatorplotGraphplotManhattanplotMCMCdiagplotNetwork
Dependencies:clicpp11digestfilehashglueigraphlatticelifecyclemagrittrMatrixpkgconfigpngRcppRcppArmadillorlangtikzDevicevctrsxml2
BayesSUR with random effects
Rendered fromBayesSUR-RE.Rmdusingknitr::rmarkdownon Jan 02 2025.Last update: 2024-08-05
Started: 2023-11-30
BayesSUR: An R package for high-dimensional multivariate Bayesian variable and covariance selection in linear regression
Rendered fromBayesSUR.pdf.asisusingR.rsp::asison Jan 02 2025.Last update: 2019-12-04
Started: 2019-12-04
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fitting BayesSUR models | BayesSUR-package BayesSUR |
| BayesSUR_internal | BayesSUR_internal |
| coef method for class 'BayesSUR' | coef.BayesSUR |
| expected log pointwise predictive density | elpd |
| Simulated data set to mimic a small expression quantitative trait loci (eQTL) example | exampleEQTL |
| Preprocessed data set to mimic a small pharmacogenomic example | exampleGDSC |
| get fitted responses | fitted.BayesSUR |
| extract the posterior mean of parameters | getEstimator |
| create a selection of plots | plot.BayesSUR |
| plot conditional predictive ordinate | plotCPO |
| plot heatmap of estimators | plotEstimator |
| plot graph for response variables | plotGraph |
| plot Manhattan-like plots | plotManhattan |
| plot MCMC diagnostic plots | plotMCMCdiag |
| plot network representation of the associations between responses and predictors | plotNetwork |
| predict method for class 'BayesSUR' | predict.BayesSUR |
| print method for class 'BayesSUR' | print.BayesSUR |
| summary method for class 'BayesSUR' | summary.BayesSUR |
| targetGene | targetGene |
